-
-

About nnsight: a transparent science API for black-box inference#

+
+

About nnsight#

+
+

An API for transparent science on black-box AI#

How can you study the internals of a deep network that is too large for you to run?
-

In this era of large-scale deep learning, the most interesting AI models are massive and hard to run. -Ordinary commercial inference service APIs let you interact with huge models, but they do not let you see model internals. -The nnsight library is different: it gives you full access to all the neural network internals. +

In this era of large-scale deep learning, the most interesting AI models are massive black boxes +that are hard to run. Ordinary commercial inference service APIs let you interact with huge +models, but they do not let you see model internals.

+

The nnsight library is different: it gives you full access to all the neural network internals. When used together with a remote service like the National Deep Inference Facility (NDIF), it lets you run expriments on huge open models easily, with full transparent access. The nnsight library is also terrific for studying smaller local models.

@@ -433,15 +436,16 @@

About nnsight: a transparent science API for black-box inference ../_images/remote_execution.png
-

An overview of the nnsight/NDIF pipeline. Researchers write simple Python code to run along with the neural network locally or remotely. Unlike with commercial inference, the experiment code can read or write any of the internal states of the neural networks being studied. This code creates a computation graph that is sent to the remote service (if needed) and then interleaved with the execution of the neural network.#

+

An overview of the nnsight/NDIF pipeline. Researchers write simple Python code to run along with the neural network locally or remotely. Unlike commercial inference, the experiment code can read or write any of the internal states of the neural networks being studied. This code creates a computation graph that can be sent to the remote service and interleaved with the execution of the neural network.#

+

How you use nnsight#

Nnsight is built on pytorch.

Running inference on a huge remote model with nnsight is very similar to running a neural network locally on your own workstataion. In fact, with nnsight, the same code for running experiments locally on small models can also be used on large models, just by changing a few arguments.

The difference between nnsight and normal inference is that when you use nnsight, you do not treat the model as an opaque black box. -Instead, you set up a python with context that enables you to get direct access to model internals while the neural network runs. +Instead, you set up a python with context that enables you to get direct access to model internals while the neural network runs. Here is how it looks:

1from nnsight import LanguageModel
 2model = LanguageModel('meta-llama/Llama-2-70b-hf')
@@ -453,11 +457,11 @@ 
How you use nnsight8print('The internal state was', hidden_state.value)
-

The library is easy to use. Any HuggingFace model can be loaded into a LanguageModel object, as you can wee on line 2. Notice we are loading a 70-billion parameter model, which is ordinarily pretty difficult to load on a regular workstation since it would take 140-280 gigabytes of GPU RAM just to store the parameters.

-

The trick that lets us work with this huge model is on line 3. We set the flag remote=True to indicate that we want to actually run the network on the remote service. By default the remote service will be NDIF. If we want to just run a smaller model quickly, we could leave it as remote=False.

-

Then when we invoke the model on line 4, we do not just call it as a function. Instead, we use it as a with context manager. The reason is that nnsight does not treat neural network models as black boxes; it provides direct access to model internals.

+

The library is easy to use. Any HuggingFace model can be loaded into a LanguageModel object, as you can wee on line 2. Notice we are loading a 70-billion parameter model, which is ordinarily pretty difficult to load on a regular workstation since it would take 140-280 gigabytes of GPU RAM just to store the parameters.

+

The trick that lets us work with this huge model is on line 3. We set the flag remote=True to indicate that we want to actually run the network on the remote service. By default the remote service will be NDIF. If we want to just run a smaller model quickly, we could leave it as remote=False.

+

Then when we invoke the model on line 4, we do not just call it as a function. Instead, we use it as a with context manager. The reason is that nnsight does not treat neural network models as black boxes; it provides direct access to model internals.

You can see what simple direct access looks like on lines 5-6. On line 5, we grab a hidden state at layer 10, and on layer 6, we change the output of an MLP module inside the transformer at layer 11.

-

When you run this with-block code on lines 5 and 6 on your local workstation, it actually creates a computation graph storing all the calculations you want to do. When the outermost with block is completed, all the defined caculations are sent to the remote server and executed there. Then when it’s all done, the results can be accessed on your local workstation as shown on line 7 and 8.

+

When you run this with-block code on lines 5 and 6 on your local workstation, it actually creates a computation graph storing all the calculations you want to do. When the outermost with block is completed, all the defined caculations are sent to the remote server and executed there. Then when it’s all done, the results can be accessed on your local workstation as shown on line 7 and 8.

What happens behind the scenes?#

@@ -469,12 +473,12 @@

What happens behind the scenes?An example of an intervention graph. Operations in research code create nodes in the graph which depend on module inputs and outputs as well as other nodes. Then, this intervention graph is interleaved with the normal computation graph of the chosen model, and requested inputs and outputs are injected into the intervention graph for execution.#

-

Basic access to model internals can give you a lot of insight about what is going on inside a large model as it runs. For example, you can use the (link needed) logit lens to read internal hidden states as text. And use can use (link needed) causal tracing to locate the layers and components within the network that play a decisive role in making a decision.

-

And you can do them on large models like Llama-2-70b!

-

The nnsight library also provies full access to gradients and optimizations methods, out of order module applications, cross prompt interventions and much more.

+

Basic access to model internals can give you a lot of insight about what is going on inside a large model as it runs. For example, you can use the logit lens to read internal hidden states as text. And use can use causal tracing or path patching or other circuit discovery methods to locate the layers and components within the network that play a decisive role in making a decision.

+

And with nnsight, you can use thes methods on large models like Llama-2-70b.

+

The nnsight library also provies full access to gradients and optimizations methods, out of order module applications, cross prompt interventions and many more features.

See the Basics and Features pages for more information on nnsight functionality.

The project is currently in Alpha pre-release and is looking for early users/and contributors!

-

If you are interested in contributing or being an early user, please join the NDIF Discord for updates, feature requests, bug reports and opportunities to help with the effort.

+

If you are interested in contributing or being an early user, join the NDIF Discord for updates, feature requests, bug reports and opportunities to help with the effort.

@@ -512,6 +516,7 @@

What happens behind the scenes? diff --git a/public/objects.inv b/public/objects.inv index b5f7d0c..355d1cc 100644 Binary files a/public/objects.inv and b/public/objects.inv differ diff --git a/public/searchindex.js b/public/searchindex.js index 608f394..a9aa5d5 100644 --- a/public/searchindex.js +++ b/public/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["about", "documentation", "documentation/contexts", "documentation/intervention", "documentation/models", "documentation/module", "documentation/patching", "documentation/tracing", "documentation/util", "index", "start", "tutorials", "tutorials/basics", "tutorials/features", "tutorials/main_demo", "tutorials/notebooks/cross_prompt", "tutorials/notebooks/multiple_token", "tutorials/notebooks/token_indexing"], "filenames": ["about.rst", "documentation.rst", "documentation/contexts.rst", "documentation/intervention.rst", "documentation/models.rst", "documentation/module.rst", "documentation/patching.rst", 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Token Based Indexing

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About nnsight: a transparent science API for black-box inference

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About nnsight